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AI CERTS® certification

AI+ Quality Assurance Practitioner™

Formerly known as AI+ Quality Assurance™ Master AI-Driven Quality Assurance: Elevate Your Testing Efficiency, Accuracy, and Scalability

AI+ Quality Assurance Practitioner™ certification badge

Course overview

Review the course before deciding whether it is suitable.

AI Testing Mastery: Gain hands-on experience with AI-powered testing tools and techniques Intelligent Automation Edge: Streamline defect detection and performance testing using intelligent automation QA Career Fast-Track: Accelerate your QA career with our comprehensive, industry-aligned exam bundle

Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
Instructor-Led: 5 days (live or virtual)Self-Paced: 40 hours of content
Exam format
50 questions, 70% passing, 90 minutes, online proctored exam
Prerequisites
Programming Skills, Basics of QA, Foundational knowledge of machine learning concepts

Course focus

Why this certification may be relevant.

01

Unlock Advanced QA Skills with AI:

Integrate AI and machine learning into testing to automate tasks, predict defects, and optimize performance.

02

Enhance Testing Efficiency and Accuracy:

Use AI tools to speed up defect detection, improve software quality, and reduce manual errors.

03

Stay Ahead in a Competitive Market:

Equip yourself with in-demand AI skills to meet industry standards and stand out in software testing.

04

Future-Proof Your Career:

Master AI technologies like NLP and defect prediction, positioning yourself for future growth in QA.

05

Real-World Application and Hands-On Experience:

Gain practical experience in AI techniques, preparing you to tackle complex QA challenges and improve software quality.

Who it may suit

Check the intended audience against your experience and goals.

Course content

Modules supplied by AI CERTs.

Open each module to review the stated topics.

01Module 1: Introduction to Quality Assurance (QA) and AI
  • 1.1 Overview of QA
  • 1.2 Introduction to AI in QA
  • 1.3 QA Metrics and KPIs
  • 1.4 Use of Data in QA
02Module 2: Fundamentals of AI, ML, and Deep Learning
  • 2.1 AI Fundamentals
  • 2.2 Machine Learning Basics
  • 2.3 Deep Learning Overview
  • 2.4 Introduction to Large Language Models (LLMs)
03Module 3: Test Automation with AI
  • 3.1 Test Automation Basics
  • 3.2 AI-Driven Test Case Generation
  • 3.3 Tools for AI Test Automation
  • 3.4 Integration into CI/CD Pipelines
04Module 4: AI for Defect Prediction and Prevention
  • 4.1 Defect Prediction Techniques
  • 4.2 Preventive QA Practices
  • 4.3 AI for Risk-Based Testing
  • 4.4 Case Study: Defect Reduction with AI
05Module 5: NLP for QA
  • 5.1 Basics of NLP
  • 5.2 NLP in QA
  • 5.3 LLMs for QA
  • 5.4 Case Study: Using NLP for Bug Triaging
06Module 6: AI for Performance Testing
  • 6.1 Performance Testing Basics
  • 6.2 AI in Performance Testing
  • 6.3 Visualization of Performance Metrics
  • 6.4 Case Study: AI in Performance Testing of a Cloud App
07Module 7: AI in Exploratory and Security Testing
  • 7.1 Exploratory Testing with AI
  • 7.2 AI in Security Testing
  • 7.3 Case Study: Enhancing Security Testing with AI
08Module 8: Continuous Testing with AI
  • 8.1 Continuous Testing Overview
  • 8.2 AI for Regression Testing
  • 8.3 Use-Case: Risk-Based Continuous Testing
09Module 9: Advanced QA Techniques with AI
  • 9.1 AI for Predictive Analytics in QA
  • 9.2 AI for Edge Cases
  • 9.3 Future Trends in AI + QA
10Module 10: Capstone Project

Tools referenced by AI CERTs

Platforms and tools included in the course information.

TensorFlowSHAP (SHapley Additive exPlanations)Amazon S3AWS SageMaker
Before you enrol

This course information is supplied through the AI CERTs catalogue. Kiwi Fern Education will confirm current availability, delivery arrangements, inclusions and fees before enrolment. The programme is not presented as a New Zealand Qualifications Authority qualification.

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